Saturday, February 06, 2016

Microsoft’s massive Turing test – are AI teachers on the horizon?

Someone
appears on social media and within three days 1.5 million people start chatting
to that person, fooling many into thinking they’re human. If you’ve seen the
movie HER – this is eerily close to that plot but it actually happened.

The plot
thickens, as it appears that Microsoft, in China, has been running a huge
Turing experiment. Microsoft’s Chinese, Bing researchers launched Xiaoice (Little
Ice) in 2014 on WeChat and Weibo. She can draw upon a deep knowledge (or access
to facts) about celebrities, sports, finance, movies… whatever. More than this
she can recite poetry, song lyrics and stories, is open, friendly, a good
listener, even a little mischievous, funny and chatty. Sentiment analysis allows
her to gauge the emotion and mood of the conversation and adapt accordingly.

Conversations

The results
were creepy. Within a few days 1.5 million people had conversations with
Xiaoice, many went for up to 10 minutes before realizing she was not human. As
the software improved, AI techniques, NLP, fed by Bing’s billions of data
points and posts, so did the level of conversational engagement. The
conversations started to get longer, with an average of 23 exchanges after tens
of millions of chats, some go on for hundreds of exchanges. At 0.5 billion
conversations and 850,000 followers, who talk to her on average, 60 times a
month, Xiaoice has proved to be a very popular companion.

Attribution

Nass &
reeves in The Media Equation, a brilliant set of 35 studies, showed that we are
gullible, in the sense that we easily attribute human qualities to technology.
We easily attribute human intention to tech, so expect, politeness, no awkward
pauses and other human qualities in our interface with tech and that’s what tech
is only now starting to deliver. Heider’s Attribution Theory also suggests
that, in terms of motivation, we attribute external and internal causes to
behavior. This we do, not only with humans but increasingly with machines.

Turing test

Xiaoice
differs from Watson and other forms of AI, in that she (see how easy it is to
slip into gender attribution to a bot) is not trying to solve a problem, like
win Jeopardy, or beat the World Champion at chess or GO. Her aim is authentic
conversation, or at least conversation that seems authentic to humans. That, in
a nutshell, is the Turing test. It may already have been passed, on a massive
scale.

AI

Even more
astonishing is that as she converses, and the data set grows, she gets better
and better. This internal learning feature, typical of such AI techniques,
means that she learns, not like a human but to behave like a human. Obviously
there is no consciousness here, that is not to say there is no intelligence.
That is a philosophical question where the question of consciousness and
intelligence may well lead to the idea that all such networks have some form of
intelligence, just not that which we know of as human.

Bot Brother

Potentially,
there’s a sinister side to this piece of AI driven tech. Big Brother really can
be a bot, but a bot designed by governments to do specific jobs, like keep the
population under control. Is it any accident that this experiment was run in
China, the master of population control? I suspect that they would never have
got away with the experiment in any liberal democracy.

Teaching technology

Will it be
possible to emulate what teachers do with technology? In many ways it already
can and does. Technology will find things out faster and with more accuracy
than a human (search). It can hold much more in memory than any human. But this
is not about simply emulating teachers subject knowledge, it is also about the
wider skills of teaching. Remember also, that teachers are humans, with brains,
and brains not only get lots of things wrong, they are full of cognitive
biases, often display racial and sexual bias, get tired, need to switch off for
around eight hours a day, start to forget and lose their powers. AI does noneof this.Where am I going with this?
I’ve been arguing for the last few years that AI is the most important
underlying trend in learning technology, as it offers the greatest
possibilities for solving the deeper problems in education and learning, such
as replication of good teaching, effective feedback, automated assessment,
motivation, access and scale.

We know from recent work, such as Todai and at
Stanford, that AI is starting to get very good at educational tasks such as
passing exams, essay marking and predicting learner attainment. It is also
delivering more effective learning experiences. This is why every major tech
company on the planet is pouring money into AI. We did not go from running speed
to 100 miles per hour by copying the leg of a cheetah – we invented the wheel.
So it is with AI. We are not copying teachers’ brains, we are building things
that may turn out to be better. Note that part of this process, with current
systems, such as essay marking or beating champions at GO, involves training
the system using real experts, the system then starts to teach itself and get
netter and better. It’s like CDP on steroids. That’s frightening.This form of AI introduces the
possibility the the ‘teacher’ component, a teacher that not only has an enormous
knowledge base, but also the human-like skills of being a motivator and
respected tutor, may be on the horizon. It’s a distant horizon, nevertheless,
it has appeared.